The Meteoritical Society collects data on meteorites that have fallen to Earth from outer space. This dataset includes the location, mass, composition, and fall year for over 45,000 meteorites that have struck our planet.
This dataset was downloaded from NASA’s Data Portal, and is based on The Meteoritical Society's Meteoritical Bulletin Database (this latter database provides additional information such as meteorite images, links to primary sources, etc.).
We explore the data as outlined in the Table Of Contents.
This is a Work In Progress. Please upvote if you like it.
library(tidyverse)
library(leaflet)
library(leaflet.extras)
library(knitr)
library(kableExtra)
rm(list=ls())
fillColor = "#FFA07A"
fillColor2 = "#F1C40F"
MetLandings = read_csv("../input/meteorite-landings.csv")
MetLandings = MetLandings %>%
# filter out weird years
filter(year>=860 & year<=2016) %>%
# filter out weird locations
filter(reclong<=180 & reclong>=-180 & (reclat!=0 | reclong!=0))We examine the most common Variable Types in this section. This is meant to introduce you to the various types of variables which will be used for Exploratory Data Analysis. This is not an exhaustive and complete set of variable types.
The following explanation has been taken from the Australian Bureau of Statistics
Numeric variables have values that describe a measurable quantity as a number, like ‘how many’ or ‘how much’. Therefore numeric variables are quantitative variables.
A continuous variable is a numeric variable. Observations can take any value between a certain set of real numbers. The value given to an observation for a continuous variable can include values as small as the instrument of measurement allows. Examples of continuous variables include height, time, age, and temperature.
A discrete variable is a numeric variable. Observations can take a value based on a count from a set of distinct whole values. A discrete variable cannot take the value of a fraction between one value and the next closest value. Examples of discrete variables include the number of registered cars, number of business locations, and number of children in a family, all of of which measured as whole units (i.e. 1, 2, 3 cars).
The data collected for a numeric variable are quantitative data.
Categorical variables have values that describe a ‘quality’ or ‘characteristic’ of a data unit, like ‘what type’ or ‘which category’. Categorical variables fall into mutually exclusive (in one category or in another) and exhaustive (include all possible options) categories. Therefore, categorical variables are qualitative variables and tend to be represented by a non-numeric value.
Categorical variables may be further described as ordinal or nominal:
An ordinal variable is a categorical variable. Observations can take a value that can be logically ordered or ranked. The categories associated with ordinal variables can be ranked higher or lower than another, but do not necessarily establish a numeric difference between each category. Examples of ordinal categorical variables include academic grades (i.e. A, B, C), clothing size (i.e. small, medium, large, extra large) and attitudes (i.e. strongly agree, agree, disagree, strongly disagree).
A nominal variable is a categorical variable. Observations can take a value that is not able to be organised in a logical sequence. Examples of nominal categorical variables include sex, business type, eye colour, religion and brand.
We explore 6 simple Graph Types.
The Bar Plot examines the distribution of a Categorical Variable.
The Histogram examines the distribution of a Continuous Variable.
When we have a Continous variable and a Categorical variable and we wish to examine the distribution of the Continous variable seperately for each of the categorical variables, a box plot can be used.
A Scatter Plot has points that show the relationship between two Continuous variables.
When we wish to seperate the distribution of a variable ( categorical or continous ) based on another categorical variable, a facet is used. A facet histogram and a facet barplot can be used.
We overlay the data on the latitude and longitude of the world and see various patterns based on this.
kable(head(MetLandings,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Aachen | 1 | Valid | L5 | 21 | Fell | 1880 | 50.77500 | 6.08333 | (50.775000, 6.083330) |
| Aarhus | 2 | Valid | H6 | 720 | Fell | 1951 | 56.18333 | 10.23333 | (56.183330, 10.233330) |
| Abee | 6 | Valid | EH4 | 107000 | Fell | 1952 | 54.21667 | -113.00000 | (54.216670, -113.000000) |
| Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 1976 | 16.88333 | -99.90000 | (16.883330, -99.900000) |
| Achiras | 370 | Valid | L6 | 780 | Fell | 1902 | -33.16667 | -64.95000 | (-33.166670, -64.950000) |
| Adhi Kot | 379 | Valid | EH4 | 4239 | Fell | 1919 | 32.10000 | 71.80000 | (32.100000, 71.800000) |
We plot the Twenty most occcuring Meteorities in a flipped bar plot.
MeteoritesCount = MetLandings %>%
group_by(recclass) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
ungroup() %>%
mutate(recclass = reorder(recclass,Count)) %>%
head(20)
MeteoritesCount %>%
ggplot(aes(x = recclass,y = Count)) +
geom_bar(stat='identity',colour="white", fill = fillColor) +
geom_text(aes(x = recclass, y = 1, label = paste0("(",Count,")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Meteorites Class',
y = 'Count',
title = 'Meteorites Class and Count') +
coord_flip() +
theme_bw()kable(head(MeteoritesCount,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| recclass | Count |
|---|---|
| L6 | 6583 |
| H5 | 5611 |
| H4 | 3335 |
| H6 | 3232 |
| L5 | 2746 |
| LL5 | 1897 |
We plot the Twenty most heavy Meteorities based on their median mass in a flipped bar plot.
MetHeavyMed = MetLandings %>%
mutate(mass = mass/1e3) %>%
group_by(recclass) %>%
summarise(MassMed = median(mass)) %>%
arrange(desc(MassMed)) %>%
ungroup() %>%
mutate(recclass = reorder(recclass,MassMed)) %>%
head(20)
MetHeavyMed %>%
ggplot(aes(x = recclass,y = MassMed)) +
geom_bar(stat='identity',colour="white", fill = fillColor) +
geom_text(aes(x = recclass, y = 1, label = paste0("(",round(MassMed),")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Meteorites Class',
y = 'Mass Median',
title = 'Meteorites Class and Mass Median') +
coord_flip() +
theme_bw()kable(head(MetHeavyMed,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| recclass | MassMed |
|---|---|
| Iron, IC | 683 |
| Mesosiderite-A3/4 | 320 |
| Mesosiderite-C | 218 |
| CR-an | 114 |
| Mesosiderite-B4 | 100 |
| Iron, IIIAB-an | 63 |
We plot the Twenty most heavy Meteorities based on their mean mass in a flipped bar plot.
MetHeavyMean = MetLandings %>%
mutate(mass = mass/1e3) %>%
group_by(recclass) %>%
summarise(MassMean = mean(mass)) %>%
arrange(desc(MassMean)) %>%
ungroup() %>%
mutate(recclass = reorder(recclass,MassMean)) %>%
head(20)
MetHeavyMean %>%
ggplot(aes(x = recclass,y = MassMean)) +
geom_bar(stat='identity',colour="white", fill = fillColor) +
geom_text(aes(x = recclass, y = 1, label = paste0("(",round(MassMean),")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Meteorites Class',
y = 'Mass Mean',
title = 'Meteorites Class and Mass Mean') +
coord_flip() +
theme_bw()kable(head(MetHeavyMean,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| recclass | MassMean |
|---|---|
| Iron, IVB | 4322.8329 |
| Iron, IIIE | 2409.6104 |
| Iron, IAB-MG | 1470.2724 |
| Iron, IC | 991.1222 |
| Iron, IAB-ung | 769.3243 |
| Mesosiderite-A1 | 698.2063 |
Here Mass is a continous variable and therfore for the distribution we plot a histogram.
We plot the distribution of the Mass of the Meteorites.
MetLandings %>%
ggplot(aes(x = mass) )+
geom_histogram(fill = fillColor2) +
scale_x_log10() +
scale_y_log10() +
labs(x = 'Mass in gms' ,y = 'Count', title = paste("Distribution of", "mass")) +
theme_bw()The mass is in Kilograms.
MetHeaviest = max(MetLandings$mass,na.rm = TRUE)
MetHeviestRec = MetLandings %>%
filter(mass == MetHeaviest) %>%
mutate( mass = mass/1e3)
kable(MetHeviestRec,"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Hoba | 11890 | Valid | Iron, IVB | 60000 | Found | 1920 | -19.58333 | 17.91667 | (-19.583330, 17.916670) |
The mass is in Kilograms.
MetLightest = min(MetLandings$mass,na.rm = TRUE)
MetLightestRec = MetLandings %>%
filter(mass == MetLightest) %>%
mutate( mass = mass/1e3)
kable(head(MetLightestRec,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Gove | 52859 | Relict | Relict iron | 0 | Found | 1979 | -12.26333 | 136.83833 | (-12.263330, 136.838330) |
| Österplana 048 | 56147 | Relict | Relict OC | 0 | Found | 2004 | 58.58333 | 13.43333 | (58.583330, 13.433330) |
| Österplana 049 | 56148 | Relict | Relict OC | 0 | Found | 2012 | 58.58333 | 13.43333 | (58.583330, 13.433330) |
| Österplana 050 | 56149 | Relict | Relict OC | 0 | Found | 2003 | 58.58333 | 13.43333 | (58.583330, 13.433330) |
| Österplana 051 | 56150 | Relict | Relict OC | 0 | Found | 2006 | 58.58333 | 13.43333 | (58.583330, 13.433330) |
| Österplana 052 | 56151 | Relict | Relict OC | 0 | Found | 2006 | 58.58333 | 13.43333 | (58.583330, 13.433330) |
The mass is in Kilograms.
MetLandingsValid = MetLandings %>%
filter(nametype == 'Valid')
MetLightest = min(MetLandingsValid$mass,na.rm = TRUE)
MetLightestRec = MetLandingsValid %>%
filter(mass == MetLightest) %>%
mutate( mass = mass/1e3)
kable(head(MetLightestRec,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Yamato 8333 | 29438 | Valid | H5 | 1e-05 | Found | 1983 | -71.5 | 35.66667 | (-71.500000, 35.666670) |
We plot the distribution of the Mass of the Meteorites based on their Fall Type
MetLandings %>%
ggplot(aes(x = mass, fill = fall)) +
geom_histogram(alpha = 0.8) +
scale_x_log10() +
scale_y_log10() +
labs(x= 'Mass in gms',y = 'Count', title = paste("Distribution of", ' mass ')) +
theme_bw()We plot the distribution of the Mass of the Meteorites based on their Fall Type. Here we manually select the colors of the fall type.
MetLandings %>%
ggplot(aes(x = mass, fill = fall)) +
geom_histogram(alpha = 0.8) +
scale_x_log10() +
scale_y_log10() +
scale_fill_manual( values = c("red","blue") )+
labs(x= 'Mass in gms',y = 'Count', title = paste("Distribution of", ' mass ')) +
theme_bw()Here Mass is a continous variable and Fall is a categorical variable. To examine the relationships between a continous and categorical variable, we plot a facet bar plot.
MetLandings %>%
ggplot(aes(x = mass, fill = fall)) +
geom_histogram(alpha = 0.8) +
scale_x_log10() +
scale_y_log10() +
scale_fill_manual( values = c("red","blue") ) +
facet_wrap(~fall) +
labs(x= 'Mass in gms',y = 'Count', title = paste("Distribution of", ' mass ')) +
theme_bw()Here Mass is a continous variable and Fall is a categorical variable. To examine the relationships between a continous and categorical variable, we plot a BoxPlot plot.
In this case, we do a BoxPlot with the mass being transformed into Kilograms. The plot shows a number of outliers and the distribution of the mass for each fall type is not very clearly observed.
MetLandings %>%
mutate( fill = as.factor(fall)) %>%
ggplot(aes(x = fall, y= mass/1e3, fill = fall)) +
geom_boxplot() +
scale_fill_manual( values = c("red","blue") ) +
facet_wrap(~fall) +
labs(x= 'Fall Type',y = 'Mass in Kgs', title = paste("Distribution of", ' mass ')) +
theme_bw()We filter the mass of the meteorites having less than 30kgs and do a boxplot.
MetLandings %>%
mutate( fill = as.factor(fall)) %>%
filter( (mass/1e3) < 30) %>%
ggplot(aes(x = fall, y= mass/1e3, fill = fall)) +
geom_boxplot() +
scale_fill_manual( values = c("red","blue") ) +
facet_wrap(~fall) +
labs(x= 'Fall Type',y = 'Mass in Kgs', title = paste("Distribution of", ' mass ')) +
theme_bw()The following plot shows the distribution of the meteorite landings all over the world.
center_lon = median(MetLandings$reclong,na.rm = TRUE)
center_lat = median(MetLandings$reclat,na.rm = TRUE)
leaflet(MetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,
color = c("red")) %>%
# controls
setView(lng=0, lat=0,zoom = 2)The mass of the Meteorites are indicated by the Radius of the Circles.
factpal <- colorFactor(c("red","blue"),
MetLandings$fall)
leaflet(MetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,radius = ~(mass/1e3)*10 ,
color = ~factpal(fall)) %>%
# controls
setView(lng=0, lat=0,zoom = 2) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)The following plot shows the distribution of the Indian meteorite landings.Here we have filtered the Indian meteorite landings by filtering the latitude and longitude.
IndiaMetLandings = MetLandings %>%
filter(reclat > 8) %>%
filter ( reclat < 38) %>%
filter( reclong > 68 ) %>%
filter(reclong < 98)
center_lon = median(IndiaMetLandings$reclong,na.rm = TRUE)
center_lat = median(IndiaMetLandings$reclat,na.rm = TRUE)
leaflet(IndiaMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,
color = c("red")) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 5) IndiaMetLandings = IndiaMetLandings %>% arrange(desc(mass))
kable(head(IndiaMetLandings,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Sulagiri | 48951 | Valid | LL6 | 110000 | Fell | 2008 | 12.66667 | 78.03333 | (12.666670, 78.033330) |
| Parnallee | 18108 | Valid | LL3.6 | 77600 | Fell | 1857 | 9.23333 | 78.35000 | (9.233330, 78.350000) |
| Merua | 15492 | Valid | H5 | 71400 | Fell | 1920 | 25.48333 | 81.98333 | (25.483330, 81.983330) |
| Mahadevpur | 47361 | Valid | H4/5 | 70500 | Fell | 2007 | 27.66667 | 95.78333 | (27.666670, 95.783330) |
| Rahimyar Khan | 31302 | Valid | L5 | 67225 | Fell | 1983 | 28.22500 | 70.20000 | (28.225000, 70.200000) |
| Dhajala | 6698 | Valid | H3.8 | 45000 | Fell | 1976 | 22.37778 | 71.42722 | (22.377780, 71.427220) |
The Different Fall Types are classified by the Red and Blue Colours.
factpal <- colorFactor(c("red","blue"),
IndiaMetLandings$fall)
leaflet(IndiaMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,
color = ~factpal(fall)) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 5) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)The mass of the Meteorites are indicated by the Radius of the Circles.
factpal <- colorFactor(c("red","blue"),
IndiaMetLandings$fall)
leaflet(IndiaMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,radius = ~(mass) ,
color = ~factpal(fall)) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 5) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)The intensity of the Heatmap is based on the mass of the meteorites.
IndiaMetLandings %>% leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addHeatmap(lng = ~reclong, lat = ~reclat, intensity = ~mass,
blur = 20, max = 0.05, radius = 15) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 5) The meteorite landings have been clustered and their numbers are being shown in the map.
IndiaMetLandings %>% leaflet() %>% addProviderTiles("Esri.OceanBasemap") %>%
addMarkers(lng = ~reclong, lat = ~reclat,clusterOptions = markerClusterOptions()) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 5) The following plot shows the distribution of the US meteorite landings.Here we have filtered the US meteorite landings by filtering the latitude and longitude.
top = 49.3457868 # north lat
left = -124.7844079 # west long
right = -66.9513812 # east long
bottom = 24.7433195 # south lat
USMetLandings = MetLandings %>%
filter(reclat >= bottom) %>%
filter ( reclat <= top) %>%
filter( reclong >= left ) %>%
filter(reclong <= right)
center_lon = median(USMetLandings$reclong,na.rm = TRUE)
center_lat = median(USMetLandings$reclat,na.rm = TRUE)
leaflet(USMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,
color = c("blue")) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) USMetLandings = USMetLandings %>% arrange(desc(mass))
kable(head(USMetLandings,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Canyon Diablo | 5257 | Valid | Iron, IAB-MG | 30000000 | Found | 1891 | 35.05000 | -111.03333 | (35.050000, -111.033330) |
| Chupaderos | 5363 | Valid | Iron, IIIAB | 24300000 | Found | 1852 | 27.00000 | -105.10000 | (27.000000, -105.100000) |
| Bacubirito | 4919 | Valid | Iron, ungrouped | 22000000 | Found | 1863 | 26.20000 | -107.83333 | (26.200000, -107.833330) |
| Willamette | 24269 | Valid | Iron, IIIAB | 15500000 | Found | 1902 | 45.36667 | -122.58333 | (45.366670, -122.583330) |
| Morito | 16745 | Valid | Iron, IIIAB | 10100000 | Found | 1600 | 27.05000 | -105.43333 | (27.050000, -105.433330) |
| Brenham | 5136 | Valid | Pallasite, PMG-an | 4300000 | Found | 1882 | 37.58250 | -99.16361 | (37.582500, -99.163610) |
The mass of the Meteorites are indicated by the Radius of the Circles.
The mass has been converted to Kgs and then multiplied by 10
factpal <- colorFactor(c("red","blue"),
USMetLandings$fall)
leaflet(USMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,radius = ~(mass/1e3)*10 ,
color = ~factpal(fall)) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)The intensity of the Heatmap is based on the mass of the meteorites.
USMetLandings %>% leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addHeatmap(lng = ~reclong, lat = ~reclat, intensity = ~mass,
blur = 20, max = 0.05, radius = 15) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) The meteorite landings have been clustered and their numbers are being shown in the map.
USMetLandings %>% leaflet() %>% addProviderTiles("Esri.OceanBasemap") %>%
addMarkers(lng = ~reclong, lat = ~reclat,clusterOptions = markerClusterOptions()) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) The mass of the Meteorites are indicated by the Radius of the Circles.The mass has been converted to Kgs and then multiplied by 10
top = 20 # north lat
left = -20 # west long
right = 50 # east long
bottom = -40 # south lat
AfricaMetLandings = MetLandings %>%
filter(reclat >= bottom) %>%
filter ( reclat <= top) %>%
filter( reclong >= left ) %>%
filter(reclong <= right)
center_lon = median(AfricaMetLandings$reclong,na.rm = TRUE)
center_lat = median(AfricaMetLandings$reclat,na.rm = TRUE)
factpal <- colorFactor(c("red","blue"),
AfricaMetLandings$fall)
leaflet(AfricaMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,radius = ~(mass/1e3)*10 ,
color = ~factpal(fall)) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 3) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)AfricaMetLandings = AfricaMetLandings %>% arrange(desc(mass))
kable(head(AfricaMetLandings,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Hoba | 11890 | Valid | Iron, IVB | 60000000 | Found | 1920 | -19.58333 | 17.91667 | (-19.583330, 17.916670) |
| Gibeon | 10912 | Valid | Iron, IVA | 26000000 | Found | 1836 | -25.50000 | 18.00000 | (-25.500000, 18.000000) |
| Mbosi | 15456 | Valid | Iron, ungrouped | 16000000 | Found | 1930 | -9.11667 | 33.06667 | (-9.116670, 33.066670) |
| Kouga Mountains | 12352 | Valid | Iron, IIIAB | 1173000 | Found | 1903 | -33.61667 | 24.00000 | (-33.616670, 24.000000) |
| Rateldraai | 22397 | Valid | Iron, IIIAB | 549000 | Found | 1909 | -28.83333 | 21.13333 | (-28.833330, 21.133330) |
| Kokstad | 12341 | Valid | Iron, IIIE | 341000 | Found | 1884 | -30.55000 | 29.41667 | (-30.550000, 29.416670) |
The intensity of the Heatmap is based on the mass of the meteorites.
AfricaMetLandings %>% leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addHeatmap(lng = ~reclong, lat = ~reclat, intensity = ~mass,
blur = 20, max = 0.05, radius = 15) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 3) The meteorite landings have been clustered and their numbers are being shown in the map.
AfricaMetLandings %>% leaflet() %>% addProviderTiles("Esri.OceanBasemap") %>%
addMarkers(lng = ~reclong, lat = ~reclat,clusterOptions = markerClusterOptions()) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 3) The mass of the Meteorites are indicated by the Radius of the Circles.The mass has been converted to Kgs and then multiplied by 10
top = -45 # north lat
left = 110 # west long
right = 145 # east long
bottom = -10 # south lat
AusMetLandings = MetLandings %>%
filter(reclat <=bottom) %>%
filter ( reclat >= top) %>%
filter( reclong >= left ) %>%
filter(reclong <= right)
center_lon = median(AusMetLandings$reclong,na.rm = TRUE)
center_lat = median(AusMetLandings$reclat,na.rm = TRUE)
factpal <- colorFactor(c("red","blue"),
AusMetLandings$fall)
leaflet(AusMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,radius = ~(mass/1e3)*10 ,
color = ~factpal(fall)) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)AusMetLandings = AusMetLandings %>% arrange(desc(mass))
kable(head(AusMetLandings,6),"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "800px")| name | id | nametype | recclass | mass | fall | year | reclat | reclong | GeoLocation |
|---|---|---|---|---|---|---|---|---|---|
| Mundrabilla | 16852 | Valid | Iron, IAB-ung | 24000000 | Found | 1911 | -30.78333 | 127.5500 | (-30.783330, 127.550000) |
| Youndegin | 30374 | Valid | Iron, IAB-MG | 3800000 | Found | 1884 | -32.10000 | 117.7167 | (-32.100000, 117.716670) |
| Huckitta | 11922 | Valid | Pallasite, PMG-an | 2300000 | Found | 1924 | -22.36667 | 135.7667 | (-22.366670, 135.766670) |
| Henbury | 11872 | Valid | Iron, IIIAB | 2000000 | Found | 1931 | -24.56667 | 133.1667 | (-24.566670, 133.166670) |
| Murnpeowie | 16878 | Valid | Iron, IC | 1143000 | Found | 1909 | -29.58333 | 139.9000 | (-29.583330, 139.900000) |
| Wolf Creek | 24326 | Valid | Iron, IIIAB | 760000 | Found | 1947 | -19.30000 | 127.7667 | (-19.300000, 127.766670) |
The intensity of the Heatmap is based on the mass of the meteorites.
AusMetLandings %>% leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addHeatmap(lng = ~reclong, lat = ~reclat, intensity = ~mass,
blur = 20, max = 0.05, radius = 15) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) The meteorite landings have been clustered and their numbers are being shown in the map.
AusMetLandings %>% leaflet() %>% addProviderTiles("Esri.OceanBasemap") %>%
addMarkers(lng = ~reclong, lat = ~reclat,clusterOptions = markerClusterOptions()) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) MetLandings %>%
group_by(year) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
ungroup() %>%
mutate(year = reorder(year,Count)) %>%
head(20) %>%
ggplot(aes(x = year,y = Count)) +
geom_bar(stat='identity',colour="white", fill = fillColor2) +
geom_text(aes(x = year, y = 1, label = paste0("(",Count,")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Meteorites Year',
y = 'Count',
title = 'Meteorites Year and Count') +
coord_flip() +
theme_bw()The time series shows the Count of the Meteorites and their corresponding Years.
MetLandings %>%
filter(year >= 1970) %>%
group_by(year) %>%
summarise(Count = n()) %>%
arrange(year) %>%
ggplot(aes(x = year,y = Count)) +
geom_bar(stat='identity',colour="white", fill = fillColor) +
labs(x = 'Meteorites Year',
y = 'Count',
title = 'Meteorites Year and Count') +
theme_bw()The plot shows the Years which have experienced the Top Twenty Heavy Meteorites.The mass of the meteorities in this plot is measured in Kilograms.
MetLandings %>%
mutate( mass = mass/1e3) %>%
group_by(year) %>%
summarise(MassMed = median(mass)) %>%
arrange(desc(MassMed)) %>%
ungroup() %>%
mutate(year = reorder(year,MassMed)) %>%
head(20) %>%
ggplot(aes(x = year,y = MassMed)) +
geom_bar(stat='identity',colour="white", fill = fillColor2) +
geom_text(aes(x = year, y = 1, label = paste0("(",round(MassMed),")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Meteorites Class',
y = 'Mass Median',
title = 'Meteorites Year and Mass Median') +
coord_flip() +
theme_bw()The plot shows the Years which have experienced the Top Twenty Heavy Meteorites. The mass of the meteorities in this plot is measured in Kilograms.
MetLandings %>%
mutate( mass = mass/1e3) %>%
filter(year > 1900) %>%
group_by(year) %>%
summarise(MassMed = median(mass)) %>%
arrange(desc(MassMed)) %>%
ungroup() %>%
mutate(year = reorder(year,MassMed)) %>%
head(20) %>%
ggplot(aes(x = year,y = MassMed)) +
geom_bar(stat='identity',colour="white", fill = fillColor) +
geom_text(aes(x = year, y = 1, label = paste0("(",round(MassMed),")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Meteorites Year',
y = 'Mass Median',
title = 'Meteorites Year and Mass Median') +
coord_flip() +
theme_bw()The following plot shows the distribution of the L6 meteorite landings all over the world.
MetLandings_L6 = MetLandings %>%
filter(recclass == 'L6')
center_lon = median(MetLandings_L6$reclong,na.rm = TRUE)
center_lat = median(MetLandings_L6$reclat,na.rm = TRUE)
leaflet(MetLandings_L6) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,
color = c("red")) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 1)summary(MetLandings_L6$mass)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 7.6 34.3 1694.6 202.0 564000.0 6
The time series shows the Count of the L6 Meteorites and their corresponding Years.
MetLandings_L6 %>%
filter(year >= 1970) %>%
group_by(year) %>%
summarise(Count = n()) %>%
arrange(year) %>%
ggplot(aes(x = year,y = Count)) +
geom_bar(stat='identity',colour="white", fill = fillColor) +
labs(x = 'Meteorites Year',
y = 'Count',
title = 'Meteorites Year and Count') +
theme_bw()The mass of the Meteorites are indicated by the Radius of the Circles.
top = 49.3457868 # north lat
left = -124.7844079 # west long
right = -66.9513812 # east long
bottom = 24.7433195 # south lat
USMetLandings = MetLandings_L6 %>%
filter(reclat >= bottom) %>%
filter ( reclat <= top) %>%
filter( reclong >= left ) %>%
filter(reclong <= right)
center_lon = median(USMetLandings$reclong,na.rm = TRUE)
center_lat = median(USMetLandings$reclat,na.rm = TRUE)
factpal <- colorFactor(c("red","blue"),
USMetLandings$fall)
leaflet(USMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,radius = ~(mass) ,
color = ~factpal(fall)) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 4) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)The mass of the Meteorites are indicated by the Radius of the Circles.
IndiaMetLandings = IndiaMetLandings %>% filter(recclass == 'L6')
center_lon = median(IndiaMetLandings$reclong,na.rm = TRUE)
center_lat = median(IndiaMetLandings$reclat,na.rm = TRUE)
factpal <- colorFactor(c("red"),
IndiaMetLandings$fall)
leaflet(IndiaMetLandings) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(lng = ~reclong, lat = ~reclat,radius = ~(mass) ,
color = ~factpal(fall)) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 5) %>%
addLegend("bottomright", pal = factpal, values = ~fall,
title = "Meteorites landings and fall",
opacity = 1)